In today's fast moving world, there is a desire for reliable, real-time spatio-temporal data (i.e., data relating to space and/or time) in a variety of scenarios. For example, vehicles (e.g., automobiles, aircrafts) may desire spatio-temporal data related to their future travels, such as the presence of airspace or roadway hazards and/or traffic. Other examples are possible as well.
Hazardous airspace conditions (e.g., inclement weather) may especially be relevant to aircrafts, because the hazards may present a variety of problems for aircraft. For example, inclement weather may damage an aircraft, jeopardize the safety of aircraft operators and passengers, and/or increase fuel costs. As such, in-flight aircraft need the ability to detect and/or obtain real-time hazard information to avoid the hazardous conditions.
Aircraft traditionally use on-board radar systems as one method for detecting and avoiding hazardous airspace conditions, such as inclement weather. On-board radar systems typically provide aircraft operators with a visual representation of hazards relative to the aircraft's position. However, the hazard detection range of a radar system in a typical aircraft is limited by inherent hardware characteristics (e.g., power, reflectance, attenuation). For example, an aircraft radar system may only be capable of detecting hazards over a range of 200 miles, and sometimes much less depending on current hazard conditions.
Aircraft may also obtain hazard information from ground-based radar systems. These ground-based radar systems may periodically collect airspace hazard information from various sources and then communicate the hazard information to aircraft. However, hazard information from these ground-based radar systems may suffer from high latency and may also be available only over land. Accordingly, there is a need for a spatio-temporal data detection system that provides reliable, real-time hazard information over a larger airspace range.